Modelli non lineari statici e dinamici su dati microeconomici: analisi delle condizioni finanziarie delle famiglie italiane

The thesis studies the economic and financial conditions of Italian households, by using microeconomic data of the Survey on Household Income and Wealth (SHIW) over the period 1998-2006. It develops along two lines of enquiry. First it studies the determinants of households holdings of assets and liabilities and estimates their correlation degree. After a review of the literature, it estimates two non-linear multivariate models on the interactions between assets and liabilities with repeated cross-sections. Second, it analyses households financial difficulties. It defines a quantitative measure of financial distress and tests, by means of non-linear dynamic probit models, whether the probability of experiencing financial difficulties is persistent over time. Chapter 1 provides a critical review of the theoretical and empirical literature on the estimation of assets and liabilities holdings, on their interactions and on households net wealth. The review stresses the fact that a large part of the literature explain households debt holdings as a function, among others, of net wealth, an assumption that runs into possible endogeneity problems. Chapter 2 defines two non-linear multivariate models to study the interactions between assets and liabilities held by Italian households. Estimation refers to a pooling of cross-sections of SHIW. The first model is a bivariate tobit that estimates factors affecting assets and liabilities and their degree of correlation with results coherent with theoretical expectations. To tackle the presence of non normality and heteroskedasticity in the error term, generating non consistent tobit estimators, semi-parametric estimates are provided that confirm the results of the tobit model. The second model is a quadrivariate probit on three different assets (safe, risky and real) and total liabilities; the results show the expected patterns of interdependence suggested by theoretical considerations. Chapter 3 reviews the methodologies for estimating non-linear dynamic panel data models, drawing attention to the problems to be dealt with to obtain consistent estimators. Specific attention is given to the initial condition problem raised by the inclusion of the lagged dependent variable in the set of explanatory variables. The advantage of using dynamic panel data models lies in the fact that they allow to simultaneously account for true state dependence, via the lagged variable, and unobserved heterogeneity via individual effects specification. Chapter 4 applies the models reviewed in Chapter 3 to analyse financial difficulties of Italian households, by using information on net wealth as provided in the panel component of the SHIW. The aim is to test whether households persistently experience financial difficulties over time. A thorough discussion is provided of the alternative approaches proposed by the literature (subjective/qualitative indicators versus quantitative indexes) to identify households in financial distress. Households in financial difficulties are identified as those holding amounts of net wealth lower than the value corresponding to the first quartile of net wealth distribution. Estimation is conducted via four different methods: the pooled probit model, the random effects probit model with exogenous initial conditions, the Heckman model and the recently developed Wooldridge model. Results obtained from all estimators accept the null hypothesis of true state dependence and show that, according with the literature, less sophisticated models, namely the pooled and exogenous models, over-estimate such persistence.

An interdisciplinary analysis of obesity: theory and empirical evidence

The dissertation is structured in three parts. The first part compares US and EU agricultural policies since the end of WWII. There is not enough evidence for claiming that agricultural support has a negative impact on obesity trends. I discuss the possibility of an exchange in best practices to fight obesity. There are relevant economic, societal and legal differences between the US and the EU. However, partnerships against obesity are welcomed. The second part presents a socio-ecological model of the determinants of obesity. I employ an interdisciplinary model because it captures the simultaneous influence of several variables. Obesity is an interaction of pre-birth, primary and secondary socialization factors. To test the significance of each factor, I use data from the National Longitudinal Survey of Adolescent Health. I compare the average body mass index across different populations. Differences in means are statistically significant. In the last part I use the National Survey of Children Health. I analyze the effect that family characteristics, built environment, cultural norms and individual factors have on the body mass index (BMI). I use Ordered Probit models and I calculate the marginal effects. I use State and ethnicity fixed effects to control for unobserved heterogeneity. I find that southern US States tend have on average a higher probability of being obese. On the ethnicity side, White Americans have a lower BMI respect to Black Americans, Hispanics and American Indians Native Islanders; being Asian is associated with a lower probability of being obese. In neighborhoods where trust level and safety perception are higher, children are less overweight and obese. Similar results are shown for higher level of parental income and education. Breastfeeding has a negative impact. Higher values of measures of behavioral disorders have a positive and significant impact on obesity, as predicted by the theory.

In defence of modeling simultaneity for a correct approximation of cultural aspects: implications for food consumers studies with latent variables

Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.

Essays in Macroecometrics: methodological aspects and empirical applications

In the first chapter, I develop a panel no-cointegration test which extends Pesaran, Shin and Smith (2001)'s bounds test to the panel framework by considering the individual regressions in a Seemingly Unrelated Regression (SUR) system. This allows to take into account unobserved common factors that contemporaneously affect all the units of the panel and provides, at the same time, unit-specific test statistics. Moreover, the approach is particularly suited when the number of individuals of the panel is small relatively to the number of time series observations. I develop the algorithm to implement the test and I use Monte Carlo simulation to analyze the properties of the test. The small sample properties of the test are remarkable, compared to its single equation counterpart. I illustrate the use of the test through a test of Purchasing Power Parity in a panel of EU15 countries. In the second chapter of my PhD thesis, I verify the Expectation Hypothesis of the Term Structure in the repurchasing agreements (repo) market with a new testing approach. I consider an "inexact" formulation of the EHTS, which models a time-varying component in the risk premia and I treat the interest rates as a non-stationary cointegrated system. The effect of the heteroskedasticity is controlled by means of testing procedures (bootstrap and heteroskedasticity correction) which are robust to variance and covariance shifts over time. I fi#nd that the long-run implications of EHTS are verified. A rolling window analysis clarifies that the EHTS is only rejected in periods of turbulence of #financial markets. The third chapter introduces the Stata command "bootrank" which implements the bootstrap likelihood ratio rank test algorithm developed by Cavaliere et al. (2012). The command is illustrated through an empirical application on the term structure of interest rates in the US.

Electrochemiluminescence: insights into the mechanisms and nanomaterial-based applications

Biomarkers are biological indicators of human health conditions. Their ultra-sensitive quantification is of paramount importance in clinical monitoring and early disease diagnosis. Biosensors are simple and easy-to-use analytical devices and, in their world, electrochemiluminescence (ECL) is one of the most promising analytical techniques that needs an ever-increasing sensitivity for improving its clinical effectiveness. Scope of this project was the investigation of the ECL generation mechanisms for enhancing the ECL intensity also through the identification of suitable nanostructures. The combination of nanotechnologies, microscopy and ECL has proved to be a very successful strategy to improve the analytical efficiency of ECL in one of its most promising bioanalytical approaches, the bead-based immunoassay. Nanosystems, such as [Ru(bpy)3]2+-dye-doped nanoparticles (DDSNPs) and Bodipy Carbon Nanodots, have been used to improve the sensitivity of ECL techniques thanks to their advantageous and tuneable properties, reaching a signal increase of 750% in DDSNPs-bead-based immunoassay system. In this thesis, an investigation of size and distance effects on the ECL mechanisms was carried out through the innovative combination of ECL microscopy and electrochemical mapping of radicals. It allowed the discovery of an unexpected and highly efficient mechanistic path for ECL generation at small distances from the electrode surface. It was exploited and enhanced through the addition of a branched amine DPIBA to the usual coreactant TPrA solution for enhancing the ECL efficiency until a maximum of 128%. Finally, a beads-based immunoassay and an immunosensor specific for cardiac Troponin I were built exploiting previous results and carbon nanotubes features. They created a conductive layer around beads enhancing the signal by 70% and activating an ECL mechanism unobserved before in such systems. In conclusion, the combination of ECL microscopy and nanotechnology and the deep understanding of the mechanisms responsible for the ECL emission led to a great enhancement in the signal.

Exploiting molecular simulations as an atomic-level microscope to elucidate clinically relevant cation chloride cotransporters

The cation chloride cotransporters (CCCs) represent a vital family of ion transporters, with several members implicated in significant neurological disorders. Specifically, conditions such as cerebrospinal fluid accumulation, epilepsy, Down’s syndrome, Asperger’s syndrome, and certain cancers have been attributed to various CCCs. This thesis delves into these pharmacological targets using advanced computational methodologies. I primarily employed GPU-accelerated all-atom molecular dynamics simulations, deep learning-based collective variables, enhanced sampling methods, and custom Python scripts for comprehensive simulation analyses. Our research predominantly centered on KCC1 and NKCC1 transporters. For KCC1, I examined its equilibrium dynamics in the presence/absence of an inhibitor and assessed the functional implications of different ion loading states. In contrast, our work on NKCC1 revealed its unique alternating access mechanism, termed the rocking-bundle mechanism. I identified a previously unobserved occluded state and demonstrated the transporter's potential for water permeability under specific conditions. Furthermore, I confirmed the actual water flow through its permeable states. In essence, this thesis leverages cutting-edge computational techniques to deepen our understanding of the CCCs, a family of ion transporters with profound clinical significance.